Studies of the vascular tree in the retina have applications not only in the medical field but also biometrics. The mathematical description of the retinal vasculature could help facilitate and improve the diagnosis of certain diseases, their automatic localization or to accelerate the identification and verification of individuals. The aim is to design and develop an algorithm that will automatically approximate major retinal vessels by parabolic, linear and kvartic functions. The main part of this thesis is therefore devoted to this issue, including vascular segmentation using Gabor filters, morphological erosion, thresholding, skeletonization and the resulting optimization of the approximation model. The quality of the produced algorithm is discussed in the summary.

This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.

This thesis deals with processing of retinal fundus images. Vision is the most important human sense and its injury has very serious consequences for humans. Automatic processing of retinal images increases the efficiency of medical examination and accelerates diagnoses of deseases. Retina exhibits unique characteristics for each person and thus can also be used to identify people. In this task is briefly discussed the structure and properties of each parts of the eye, particularly the retina, and their possible diseases such as diabetic retinopathy, glaucoma and age related macular degeneration. Subsequently, the task describes the representation and characteristics of the digital image. Also is devoted to selected image segmentation methods namely thresholding, edge detection and segmentation techniques based on the matched filter. The outcome of this task is the application in which several segmentation methods are implemented for the blood vessels extraction. For each of these methods it is possible to set the parameters of the segmentation to ensure high quality blood vessels extraction in images of different quality.

Diploma thesis deals with image processing for outdoor environment mobile robot. In first part, the problem is analyzed, general solution is proposed and suitable image processing methods are presented. In second part presented methods are tested and methods with best results are proposed. In third part is particular solution tested on real data.

This master´s thesis deals with processing of image sequence taken by statically placed camera over plane of robot movement. At first there are methods for image segmentation and localization methods described. In the next part, selected methods are implemented and compared to individual images. In the final part, selected methods are further implemented in algorithm for batch processing of image sequence.

Image segmentation is an important step in image processing. A traditional way how to segment an image is a texture-based segmentation that uses texture features to describe image texture. In this work, Local Binary Patterns (LBP) are used for image texture representation. Texture feature is a histogram of occurences of LBP codes in a small image window. The work also aims to comparison of results of various modifications of Local Binary Patterns and their usability in the image segmentation which is done by unsupervised clustering of texture features. The Fuzzy C-Means algorithm is finally used for the clustering in this work.

This thesis is about unusal view of implementation of graphic algorithms in FPGA in computer vision context. There are some informations about raster image and raster image operations, raster image segmentation usign threhsholding and adaptive thresholding and FPGA and DSP platforms. Next, there is a concept of the concrete project realization in the Unicam2D camera and description other ways of implementation. Next, there is a description of implemented tests with some demonstration followed by discussion of ressults in the end of the work.

This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.

Recent advances in machine learning has enabled notable progress in many aspects of image analysis. In this thesis, we present three applications to exemplify such advancement, including shadow detection, satellite image forensics and eating scene segmentation and clustering. Shadow detection and removal are of great interest to the image processing and image forensics community. In this thesis, we study automatic shadow detection from two different perspectives. First, we propose automatic methods for detecting and removing shadows in color images. Second, we present machine learning based methods to detect if shadows have been removed in an image. In the second part of the thesis, we study image forensics for satellite images. Satellite images have been subjected to various tampering and manipulations due to easy access and the availability of image manipulation tools. In this thesis, we propose methods to automatically detect and localize spliced objects in satellite images. Extracting information from the eating scene captured by images provides new means of studying the relationship between diet and health. In the third part of the thesis, we propose a class-agnostic food segmentation method that is able to segment foods without knowing the food type and a method to cluster eating scene images based on the eating environment.

During vertebrate embryo development, the body axis is subdivided into repeated structures, called somites. Somites bud off from an un-segmented tissue called the pre-somitic mesoderm (PSM) in a sequential and periodic manner, tightly controlled by an in built molecular clock, called the "segmentation clock". According to current understanding, the clock is comprised of: (i) an autonomous cellular oscillator consisting of an intracellular negative feedback loop of Her genes within the PSM cells, (ii) Delta-ligand and Notch-receptor coupling that facilitates synchronization of oscillators among the PSM cells, (iii) Tissue level waves of gene expression that emerge in the posterior PSM and move coherently towards anterior, leading to global arrest of oscillations in the form of somites. However, the movement of cellular oscillators within the PSM before the formation of somitic furrows, a prominent feature of the tissue as observed through this work has not been experimentally considered as a constituent of the segmentation clock so far. Our work aims to incorporate movement of cellular oscillators in the framework of the segmentation clock. It is well known from theoretical studies that the characteristics of relative motion of oscillators affect their synchronization properties and the patterns of oscillations they form. Particularly, theoretical studies by Uriu et al., PNAS (2010) suggest that cell movements promotes synchronization of genetic oscillations. Here, we established experimental techniques and image analysis tools to attain quantitative insight on (i) diffusion co-efficient of cellular oscillators, (ii) dynamics of a population of oscillators, within the PSM aiming towards concomitant understanding of the relationship between movement and synchronization of cellular oscillators. In order to quantitatively relate cellular oscillators and their motion within the PSM, I established imaging techniques that enabled visualization of fluorescenctly labeled nuclei as readouts of cell positions in live embryo, and developed dedicated segmentation algorithm and implemented tracking protocol to obtain nuclei positions over time in 3D space. Furthermore, I provide benchmarking techniques in the form of artificial data that validate segmentation algorithm efficacy and, for the first time proposed the use of transgenic embryo chimeras to validate segmentation algorithm performance within the context of in vivo live imaging of embryonic tissues. Preliminary analysis of our data suggests that there is relatively high cell mixing in the posterior PSM, within the same spatial zone where synchronous oscillations emerge at maximum speed. Also, there are indications of gradient of cell mixing along the anterior-posterior axis of the embryo. By sampling single cell tracks with the help of nuclei markers, we have also been able to follow in vivo protein oscillations at single cell resolution that would allow quantitative characterization of coherence among a population of cellular oscillators over time. Our image analysis work flow allows testing of mutant embryos and perturbation of synchrony dynamics to understand the cause-effect relationship between movement and synchronization properties at cellular resolution. Essentially, through this work, we hope to bridge the time scales of events and cellular level dynamics that leads to highly coordinated tissue level patterns and thereby further our understanding of the segmentation clock mechanism.